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Real-time 3D rendering using depth-based geometry reconstruction and view-dependent texture mapping

Published: 24 July 2016 Publication History

Abstract

With the recent proliferation of high-fidelity head-mounted displays (HMDs), there is increasing demand for realistic 3D content that can be integrated into virtual reality environments. However, creating photorealistic models is not only difficult but also time consuming. A simpler alternative involves scanning objects in the real world and rendering their digitized counterpart in the virtual world. Capturing objects can be achieved by performing a 3D scan using widely available consumer-grade RGB-D cameras. This process involves reconstructing the geometric model from depth images generated using a structured light or time-of-flight sensor. The colormap is determined by fusing data from multiple color images captured during the scan. Existing methods compute the color of each vertex by averaging the colors from all these images. Blending colors in this manner creates low-fidelity models that appear blurry. (Figure 1 right). Furthermore, this approach also yields textures with fixed lighting that is baked on the model. This limitation becomes more apparent when viewed in head-tracked virtual reality, as the illumination (e.g. specular reflections) does not change appropriately based on the user's viewpoint.

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References

[1]
Nakashima, Y., Uno, Y., Kawai, N., Sato, T., and Yokoya, N. 2015. Ar image generation using view-dependent geometry modification and texture mapping. Virtual Reality 19, 2, 83--94.
[2]
Zhou, Q.-Y., and Koltun, V. 2014. Color map optimization for 3d reconstruction with consumer depth cameras. ACM Trans. Graph. 33, 4 (July), 155:1--155:10.

Cited By

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  • (2023)Conception collaborative au travers de versions parallèles en Réalité AugmentéeProceedings of the 34th Conference on l'Interaction Humain-Machine10.1145/3583961.3583978(1-13)Online publication date: 3-Apr-2023
  • (2021)Transmission Method of the Rendered Image based on Cube Environment Mapping2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)10.1109/AIAM54119.2021.00070(314-317)Online publication date: Oct-2021
  • (2020)A fast fusion method for multi-videos with three-dimensional GIS scenesMultimedia Tools and Applications10.1007/s11042-020-09742-4Online publication date: 9-Sep-2020
  • Show More Cited By

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cover image ACM Conferences
SIGGRAPH '16: ACM SIGGRAPH 2016 Posters
July 2016
170 pages
ISBN:9781450343718
DOI:10.1145/2945078
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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New York, NY, United States

Publication History

Published: 24 July 2016

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Author Tags

  1. 3D reconstruction
  2. real-time rendering
  3. view-dependent texture mapping

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Cited By

View all
  • (2023)Conception collaborative au travers de versions parallèles en Réalité AugmentéeProceedings of the 34th Conference on l'Interaction Humain-Machine10.1145/3583961.3583978(1-13)Online publication date: 3-Apr-2023
  • (2021)Transmission Method of the Rendered Image based on Cube Environment Mapping2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)10.1109/AIAM54119.2021.00070(314-317)Online publication date: Oct-2021
  • (2020)A fast fusion method for multi-videos with three-dimensional GIS scenesMultimedia Tools and Applications10.1007/s11042-020-09742-4Online publication date: 9-Sep-2020
  • (2017)Novel view synthesis with light-weight view-dependent texture mapping for a stereoscopic HMD2017 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME.2017.8019417(703-708)Online publication date: Jul-2017

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